A distributionally robust scheduling strategy is proposed to address the complex benefit allocation problem in regional integrated energy systems(RIESs)with multiple stakeholders.A two-level Stackelberg game model is ...A distributionally robust scheduling strategy is proposed to address the complex benefit allocation problem in regional integrated energy systems(RIESs)with multiple stakeholders.A two-level Stackelberg game model is established,with the RIES operator as the leader and the users as the followers.It considers the interests of the RIES operator and demand response users in energy trading.The leader optimizes time-of-use(TOU)energy prices to minimize costs while users formulate response plans based on prices.A two-stage distributionally robust game model with comprehensive norm constraints,which encompasses the two-level Stackelberg game model in the day-ahead scheduling stage,is constructed to manage wind power uncertainty.Karush-Kuhn-Tucker(KKT)conditions transform the two-level Stackelberg game model into a single-level robust optimization model,which is then solved using column and constraint generation(C&CG).Numerical results demonstrate the effectiveness of the proposed strategy in balancing stakeholders'interests and mitigating wind power risks.展开更多
According to the actual requirements,profile and rolling energy consumption are selected as objective functions of rolling schedule optimization for tandem cold rolling.Because of mechanical wear,roll diameter has som...According to the actual requirements,profile and rolling energy consumption are selected as objective functions of rolling schedule optimization for tandem cold rolling.Because of mechanical wear,roll diameter has some uncertainty during the rolling process,ignoring which will cause poor robustness of rolling schedule.In order to solve this problem,a robust multi-objective optimization model of rolling schedule for tandem cold rolling was established.A differential evolution algorithm based on the evolutionary direction was proposed.The algorithm calculated the horizontal angle of the vector,which was used to choose mutation vector.The chosen vector contained converging direction and it changed the random mutation operation in differential evolution algorithm.Efficiency of the proposed algorithm was verified by two benchmarks.Meanwhile,in order to ensure that delivery thicknesses have descending order like actual rolling schedule during evolution,a modified Latin Hypercube Sampling process was proposed.Finally,the proposed algorithm was applied to the model above.Results showed that profile was improved and rolling energy consumption was reduced compared with the actual rolling schedule.Meanwhile,robustness of solutions was ensured.展开更多
Proactive scheduling based on expected value model is an effective method to develop robust schedules in consideration of minimizing project cost caused by deviations between realized and planed activity starting time...Proactive scheduling based on expected value model is an effective method to develop robust schedules in consideration of minimizing project cost caused by deviations between realized and planed activity starting times.However,these schedules may be realized with low probabilities.In this paper,a novel model based on dependent-chance programming(DCP) is proposed,considering probability as well as solution robustness.A hybrid intelligent algorithm integrating stochastic simulation and genetic algorithm(GA)is designed to solve the proposed model.Moreover,a numerical example is conducted to reveal the effectiveness of the proposed model and the algorithm.展开更多
The Vehicle Routing Problem with Time Windows(VRPTW)presents a significant challenge in combinatorial optimization,especially under real-world uncertainties such as variable travel times,service durations,and dynamic ...The Vehicle Routing Problem with Time Windows(VRPTW)presents a significant challenge in combinatorial optimization,especially under real-world uncertainties such as variable travel times,service durations,and dynamic customer demands.These uncertainties make traditional deterministic models inadequate,often leading to suboptimal or infeasible solutions.To address these challenges,this work proposes an adaptive hybrid metaheuristic that integrates Genetic Algorithms(GA)with Local Search(LS),while incorporating stochastic uncertainty modeling through probabilistic travel times.The proposed algorithm dynamically adjusts parameters—such as mutation rate and local search probability—based on real-time search performance.This adaptivity enhances the algorithm’s ability to balance exploration and exploitation during the optimization process.Travel time uncertainties are modeled using Gaussian noise,and solution robustness is evaluated through scenario-based simulations.We test our method on a set of benchmark problems from Solomon’s instance suite,comparing its performance under deterministic and stochastic conditions.Results show that the proposed hybrid approach achieves up to a 9%reduction in expected total travel time and a 40% reduction in time window violations compared to baseline methods,including classical GA and non-adaptive hybrids.Additionally,the algorithm demonstrates strong robustness,with lower solution variance across uncertainty scenarios,and converges faster than competing approaches.These findings highlight the method’s suitability for practical logistics applications such as last-mile delivery and real-time transportation planning,where uncertainty and service-level constraints are critical.The flexibility and effectiveness of the proposed framework make it a promising candidate for deployment in dynamic,uncertainty-aware supply chain environments.展开更多
The increasing interdependency of electricity and natural gas systems promotes coordination of the two systems for ensuring operational security and economics.This paper proposes a robust day-ahead scheduling model fo...The increasing interdependency of electricity and natural gas systems promotes coordination of the two systems for ensuring operational security and economics.This paper proposes a robust day-ahead scheduling model for the optimal coordinated operation of integrated energy systems while considering key uncertainties of the power system and natural gas system operation cost. Energy hub,with collocated gas-fired units, power-to-gas(Pt G) facilities, and natural gas storages, is considered to store or convert one type of energy(i.e., electricity or natural gas)into the other form, which could analogously function as large-scale electrical energy storages. The column-andconstraint generation(C&CG) is adopted to solve the proposed integrated robust model, in which nonlinear natural gas network constraints are reformulated via a set of linear constraints. Numerical experiments signify the effectiveness of the proposed model for handling volatile electrical loads and renewable generations via the coordinated scheduling of electricity and natural gas systems.展开更多
The assumption of static and deterministic conditions is common in the practice of construction project planning. However, at the construction phase, projects are subject to uncertainty. This may lead to serious sched...The assumption of static and deterministic conditions is common in the practice of construction project planning. However, at the construction phase, projects are subject to uncertainty. This may lead to serious schedule disruptions and, as a consequence, serious revisions oft.he schedule baseline. The aim of the paper is developing a method for constructing robust project schedules with a proactive procedure. Robust project scheduling allows for constructing stable schedules with time buffers introduced to cope with multiple disruptions during project execution. The method proposed by the authors, based on Monte Carlo simulation technique and mathematical programming for buffer sizing optimization, was applied to scheduling an example project. The results were compared, in terms of schedule stability, to those of the float factor heuristic procedttre.展开更多
基金supported by National Natural Science Foundation of China(No.52207133)Science and Technology Project of State Grid Corporation of China(No.5400-202112571A-0-5-SF)。
文摘A distributionally robust scheduling strategy is proposed to address the complex benefit allocation problem in regional integrated energy systems(RIESs)with multiple stakeholders.A two-level Stackelberg game model is established,with the RIES operator as the leader and the users as the followers.It considers the interests of the RIES operator and demand response users in energy trading.The leader optimizes time-of-use(TOU)energy prices to minimize costs while users formulate response plans based on prices.A two-stage distributionally robust game model with comprehensive norm constraints,which encompasses the two-level Stackelberg game model in the day-ahead scheduling stage,is constructed to manage wind power uncertainty.Karush-Kuhn-Tucker(KKT)conditions transform the two-level Stackelberg game model into a single-level robust optimization model,which is then solved using column and constraint generation(C&CG).Numerical results demonstrate the effectiveness of the proposed strategy in balancing stakeholders'interests and mitigating wind power risks.
基金funded by the Science and Technology Research Project of Education Department of Liaoning(L2015387)Natural Science Foundation of Liaoning(201602542)the National Natural Science Foundation of China(51407119)
文摘According to the actual requirements,profile and rolling energy consumption are selected as objective functions of rolling schedule optimization for tandem cold rolling.Because of mechanical wear,roll diameter has some uncertainty during the rolling process,ignoring which will cause poor robustness of rolling schedule.In order to solve this problem,a robust multi-objective optimization model of rolling schedule for tandem cold rolling was established.A differential evolution algorithm based on the evolutionary direction was proposed.The algorithm calculated the horizontal angle of the vector,which was used to choose mutation vector.The chosen vector contained converging direction and it changed the random mutation operation in differential evolution algorithm.Efficiency of the proposed algorithm was verified by two benchmarks.Meanwhile,in order to ensure that delivery thicknesses have descending order like actual rolling schedule during evolution,a modified Latin Hypercube Sampling process was proposed.Finally,the proposed algorithm was applied to the model above.Results showed that profile was improved and rolling energy consumption was reduced compared with the actual rolling schedule.Meanwhile,robustness of solutions was ensured.
基金National Natural Science Foundations of China(Nos.71371141,71001080)
文摘Proactive scheduling based on expected value model is an effective method to develop robust schedules in consideration of minimizing project cost caused by deviations between realized and planed activity starting times.However,these schedules may be realized with low probabilities.In this paper,a novel model based on dependent-chance programming(DCP) is proposed,considering probability as well as solution robustness.A hybrid intelligent algorithm integrating stochastic simulation and genetic algorithm(GA)is designed to solve the proposed model.Moreover,a numerical example is conducted to reveal the effectiveness of the proposed model and the algorithm.
文摘The Vehicle Routing Problem with Time Windows(VRPTW)presents a significant challenge in combinatorial optimization,especially under real-world uncertainties such as variable travel times,service durations,and dynamic customer demands.These uncertainties make traditional deterministic models inadequate,often leading to suboptimal or infeasible solutions.To address these challenges,this work proposes an adaptive hybrid metaheuristic that integrates Genetic Algorithms(GA)with Local Search(LS),while incorporating stochastic uncertainty modeling through probabilistic travel times.The proposed algorithm dynamically adjusts parameters—such as mutation rate and local search probability—based on real-time search performance.This adaptivity enhances the algorithm’s ability to balance exploration and exploitation during the optimization process.Travel time uncertainties are modeled using Gaussian noise,and solution robustness is evaluated through scenario-based simulations.We test our method on a set of benchmark problems from Solomon’s instance suite,comparing its performance under deterministic and stochastic conditions.Results show that the proposed hybrid approach achieves up to a 9%reduction in expected total travel time and a 40% reduction in time window violations compared to baseline methods,including classical GA and non-adaptive hybrids.Additionally,the algorithm demonstrates strong robustness,with lower solution variance across uncertainty scenarios,and converges faster than competing approaches.These findings highlight the method’s suitability for practical logistics applications such as last-mile delivery and real-time transportation planning,where uncertainty and service-level constraints are critical.The flexibility and effectiveness of the proposed framework make it a promising candidate for deployment in dynamic,uncertainty-aware supply chain environments.
基金supported in part by the U.S.National Science Foundation Grant(No.CMMI-1635339)
文摘The increasing interdependency of electricity and natural gas systems promotes coordination of the two systems for ensuring operational security and economics.This paper proposes a robust day-ahead scheduling model for the optimal coordinated operation of integrated energy systems while considering key uncertainties of the power system and natural gas system operation cost. Energy hub,with collocated gas-fired units, power-to-gas(Pt G) facilities, and natural gas storages, is considered to store or convert one type of energy(i.e., electricity or natural gas)into the other form, which could analogously function as large-scale electrical energy storages. The column-andconstraint generation(C&CG) is adopted to solve the proposed integrated robust model, in which nonlinear natural gas network constraints are reformulated via a set of linear constraints. Numerical experiments signify the effectiveness of the proposed model for handling volatile electrical loads and renewable generations via the coordinated scheduling of electricity and natural gas systems.
文摘The assumption of static and deterministic conditions is common in the practice of construction project planning. However, at the construction phase, projects are subject to uncertainty. This may lead to serious schedule disruptions and, as a consequence, serious revisions oft.he schedule baseline. The aim of the paper is developing a method for constructing robust project schedules with a proactive procedure. Robust project scheduling allows for constructing stable schedules with time buffers introduced to cope with multiple disruptions during project execution. The method proposed by the authors, based on Monte Carlo simulation technique and mathematical programming for buffer sizing optimization, was applied to scheduling an example project. The results were compared, in terms of schedule stability, to those of the float factor heuristic procedttre.
